Surveillance systems for surveillance of public squares, buildings, but also private facilities or factories, often include a plurality of video cameras that are aimed at relevant areas to be observed. The image data acquired by the surveillance cameras are typically put together centrally and evaluated.
The evaluation can be done on the one hand by surveillance personnel, but this job is stressful and fatiguing, so that surveillance mistakes from decreasing attentiveness or a lack of attention cannot be precluded. It is advantageous for this reason to evaluate the image data automatically and to detect events in the surveillance scenes on the basis of predetermined conditions.
In automated evaluation and surveillance, it is of particular interest to detect moving surveillance objects, such as persons or motor vehicles, and track them over time, so that if unusual trajectories occur or unusual behavior occurs, an alarm can be tripped. For this purpose, image processing algorithms are typically used; the moving object areas are separated in the context of an object segmentation from an essentially static scene background and are tracked over time, and if relevant motions occur, an alarm is tripped. For the object segmentation, these conventional methods typically evaluate the differences between a current camera image and a reference image of the scene that models the static or quasi-static scene background.
These image processing algorithms for separating the moving surveillance objects from a scene background usually function reliably, as long as individual surveillance objects can be distinguished from one another. In the case where the number of moving surveillance objects keeps increasing, and these surveillance objects come closer and closer together and finally even overlap, however, segmented image areas of various surveillance objects fuse into an image area of a common object or object group. Although it appears possible to detect this fusion, however, the object segmentation and tracking of surveillance objects during the fusion involves many problems and has not yet been solved to satisfaction.
German Patent Disclosure DE 101 33 386 A1 describes a method and an apparatus for detecting an actual position of a person within a predeterminable area and a use of the method and/or apparatus. The method described in this reference detects a person in a surveillance area. In the next step, a significant feature of the person is determined, and the further evaluation—particularly to save computation time—is limited solely to observation, and in particular detection and tracking, of this at least one significant feature. In contrast to the conventional image processing algorithms described above, this reference thus has to do with tracking individual features in a sequence of images.
However, since this reference discloses nothing about how to proceed in surveillance situations in which a plurality of surveillance objects are present, the closest prior art is most likely conventional image processing algorithms.